| Full text | |
| Author(s): |
Torquette, Gustavo
;
Basgalupp, Marcio P.
;
Ludermir, Teresa B.
;
Lorena, Ana Carolina
Total Authors: 4
|
| Document type: | Journal article |
| Source: | 40TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING; v. N/A, p. 8-pg., 2025-01-01. |
| Abstract | |
Instance hardness measures allow one to characterize and understand why some instances are harder to classify than others in a classification dataset. An instance can be hard to classify for different reasons, such as being in an overlapping region of the classes or a region of poor data representativeness. While there are many instance hardness measures in the related literature, they are mainly concerned with measuring class overlap. This paper also addresses measuring sparsity in a dataset by building a proximity graph from data and extracting some network-based measures from the nodes. Experimentally, we show that some of these measures are effective in characterizing instance hardness and complement the ones from the literature by measuring the density of the regions where the instances are located. (AU) | |
| FAPESP's process: | 22/07458-1 - Automatic selection and recommendation of machine learning algorithms |
| Grantee: | Márcio Porto Basgalupp |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 21/06870-3 - Beyond algorithm selection: meta-learning for data and algorithm analysis and understanding |
| Grantee: | Ana Carolina Lorena |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 20/09835-1 - IARA - Artificial Intelligence in the Remaking of Urban Environments |
| Grantee: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |